2.1 Irregularly sampled data

Consider the snapshots ${\mathcal{W}}=(\boldsymbol{w}_{n})_{n=0}^{N}\subset \mathbb{R}^{p}$ described in (2.1), sampled with constant time step $\unicode[STIX]{x1D6FF}t$ . An alternative to varying $\unicode[STIX]{x1D6FF}t$ , as discussed above, is to instead view $\unicode[STIX]{x1D6FF}t$ as a minimal time step representing the highest possible frequency of data collection, but assume that only a subset ${\mathcal{U}}\subset {\mathcal{W}}$ of snapshots may be used for analysis. We refer to ${\mathcal{U}}$ as the analysis data ensemble and define it by choosing a subset ${\mathcal{J}}\subset \{0,1,\ldots ,N\}$ , re-labelling $\boldsymbol{u}_{i}:=w_{i}$ for each index $i\in {\mathcal{J}}$ , and setting ${\mathcal{U}}=\{\boldsymbol{u}_{i}:i\in {\mathcal{J}}\}$ . Finally, let $\unicode[STIX]{x0394}t_{i}$ be the sample times between successive snapshots in ${\mathcal{U}}$ which are, possibly non-constant, integer multiples of $\unicode[STIX]{x1D6FF}t$ . The schematic representation of such a sampling strategy is presented in figure 2.

Figure 2. Downsampled and regularly sampled data.

It will be assumed that ${\mathcal{W}}$ is sampled at a sufficiently high rate that it contains all dynamical information of interest concerning the state trajectory $f(\boldsymbol{x},t)$ . The question which we aim to address is to what extent ${\mathcal{W}}$ can be reconstructed from knowledge of the analysis data set ${\mathcal{U}}$ only. Compressive sampling presents a standard method of answering such a problem.

2.2 Compressive sampling

Extracting coherent dynamical information from a downsampled data ensemble ${\mathcal{U}}$ may be possible if the underlying system is sparse, or compressible, when expressed in particular temporal basis. To describe this, first collect the high-frequency sampled snapshots ${\mathcal{W}}$ into a matrix

The state trajectory $\unicode[STIX]{x1D652}$ is said to be compressible if there exists a matrix $\unicode[STIX]{x1D729}=(\unicode[STIX]{x1D709}_{ij})\in \mathbb{R}^{(N+1)\times (N+1)}$ such that it can be approximately represented in the form

where $\hat{\unicode[STIX]{x1D652}}\in \mathbb{R}^{p\times N}$ is sparse in the sense that it has only $k$ non-zero columns, with $k\ll N$ . Letting $\hat{\boldsymbol{w}}_{i}$ denote the columns of $\hat{\boldsymbol{w}}$ , and rewriting the decomposition (2.5) as

it is clear that the each column $\hat{\boldsymbol{w}}_{j}\in \mathbb{R}^{p}$ represents a spatial structure, while the respective row $(\unicode[STIX]{x1D709}_{ji})_{i=0}^{N}$ of $\unicode[STIX]{x1D729}$ describes its temporal evolution. For this discussion we make the assumption that a temporal basis $\unicode[STIX]{x1D729}$ is known for which a columnwise sparse solution $\hat{\unicode[STIX]{x1D652}}$ to (2.5) exists.

Next, write the analysis data ensemble ${\mathcal{U}}$ in matrix form

by defining an appropriate binary matrix $\unicode[STIX]{x1D723}\in \mathbb{R}^{N+1\times m}$ , $m=|{\mathcal{J}}|$ , which encodes the particular time steps at which data are available. The aim of compressive sampling is to determine $\unicode[STIX]{x1D652}$ from knowledge of $\unicode[STIX]{x1D650}$ only. In view of (2.5) and (2.7), one way to achieve this is to seek a columnwise sparse solution $\hat{\unicode[STIX]{x1D652}}$ to

which would then provide an approximation to the full signal via $\unicode[STIX]{x1D652}\approx \hat{\unicode[STIX]{x1D652}}\unicode[STIX]{x1D729}$ .

Note that, since $m\leqslant N$ , equation (2.8) is overdetermined, meaning that the imposition of sparsity is necessary to obtain a well-posed problem. The most widely used approach to sparsity promotion is to penalise the $\ell ^{1}$ -norm of $\hat{\unicode[STIX]{x1D652}}$ subject to the constraint (2.8). However, apart from in the scalar-valued case $p=1$ , this will impose entrywise sparsity rather than the desired columnwise sparsity of $\hat{\unicode[STIX]{x1D652}}$ . Methods to avoid this problem typically use greedy algorithms such as orthogonal matching pursuit (OMP) (Tropp & Gilbert Reference Tropp and Gilbert2007) or compressive sampling matching pursuit (CoSaMP) (Needell & Tropp Reference Needell and Tropp2009). Rather than minimising the $\ell ^{1}$ -norm of $\hat{\unicode[STIX]{x1D652}}$ , such methodologies analyse the correlation of the matrix $\unicode[STIX]{x1D729}\unicode[STIX]{x1D723}$ with the downsampled data ensemble $\unicode[STIX]{x1D650}$ . They are iterative algorithms in which, upon each iteration, one sparse vector (a column of $\unicode[STIX]{x1D729}$ ) is added to the solution. Hence, the outputs of the algorithms are sparse, with the sparsity of the solution equal to the number of iterations.

An important fact is that the sampling strategy, encoded by $\unicode[STIX]{x1D723}$ , plays a significant role in determining the performance of compressive sampling algorithms. In particular, a known requirement is that the chosen temporal basis $\unicode[STIX]{x1D729}$ has a low coherence with the sampling strategy $\unicode[STIX]{x1D723}$ (where coherence is defined $\max _{j,k}|\langle (\unicode[STIX]{x1D703}_{ji})_{i=0}^{N},(\unicode[STIX]{x1D709}_{ik})_{i=0}^{N}\rangle |$ ) (Candes & Wakin Reference Candes and Wakin2008). A common way to achieve this is to use Bernoulli and Gaussian random measurements, which are known to be incoherent with many canonical bases, including the typical Fourier basis (Candes & Wakin Reference Candes and Wakin2008). That is, if periodic structures are sought in time, then an aperiodic sampling strategy is beneficial for extracting them from downsampled data. We reiterate that DMD-type modal extraction methods do not currently support sampling at non-constant time intervals, meaning that any potential benefits of such a strategy may not be realised using current techniques. We now expand upon this observation and propose an extension to DMD to enable its application to a general irregularly sampled data ensemble of the form ${\mathcal{U}}$ described in figure 2.

2.3 Dynamic decomposition of irregularly sampled data

The application of DMD to the regularly sampled data set ${\mathcal{W}}$ , as discussed above, can be obtained by minimising the cost (2.2) with respect to the dynamics matrix $\unicode[STIX]{x1D64E}$ . This cost function corresponds to projecting each snapshot $\boldsymbol{w}_{i}$ onto its POD coefficients via $\unicode[STIX]{x1D650}_{p}^{\text{T}}\boldsymbol{w}_{i}$ , transforming these coefficients under the mapping $\unicode[STIX]{x1D64E}$ , lifting the result back to create a modified flow field $\unicode[STIX]{x1D650}_{p}\unicode[STIX]{x1D64E}\unicode[STIX]{x1D650}_{p}^{\text{T}}\boldsymbol{w}_{i}$ , and finally comparing this flow field to the snapshot $\boldsymbol{w}_{i+1}$ . Consequently, DMD can be viewed in terms of constructing an optimal model of the form $\boldsymbol{w}_{i}\mapsto \unicode[STIX]{x1D650}_{p}\unicode[STIX]{x1D64E}\unicode[STIX]{x1D650}_{p}^{\text{T}}\boldsymbol{w}_{i}$ which best approximates the one-step snapshot evolution across the entire data ensemble. Solving the DMD optimisation problem is computationally undemanding: the calculation of $\unicode[STIX]{x1D650}_{p}$ is simply $\text{POD}(\boldsymbol{w}_{0},\ldots ,\boldsymbol{w}_{N-1})$ ; while, since (2.2) is quadratic, an analytical solution for $\unicode[STIX]{x1D64E}$ can easily be obtained by differentiating the cost and solving the resulting linear equation (Wynn et al. Reference Wynn, Pearson, Ganapathisubramani and Goulart2013).

Now, if one attempts to apply a similar approach to the irregularly sampled data ensemble ${\mathcal{U}}$ , a natural generalisation of (2.2) is to consider the modified cost function

where $n_{i}:=\unicode[STIX]{x0394}t_{i}/\unicode[STIX]{x1D6FF}t\in \mathbb{N}$ is the number of minimal time steps $\unicode[STIX]{x1D6FF}t$ between each sample point in ${\mathcal{U}}$ . In other words, if there are $n_{i}$ minimal time steps between available data vectors $\boldsymbol{u}_{i+1}$ and $\boldsymbol{u}_{i}$ we seek to model the system evolution over an interval of length $\unicode[STIX]{x0394}t_{i}=n_{i}\unicode[STIX]{x1D6FF}t$ by the iterated linear mapping $(\unicode[STIX]{x1D650}_{p}\unicode[STIX]{x1D64E}\unicode[STIX]{x1D650}_{p}^{\text{T}})^{n_{i}}=\unicode[STIX]{x1D650}_{p}\unicode[STIX]{x1D64E}^{n_{i}}\unicode[STIX]{x1D650}_{p}^{\text{T}}$ .

Such a formulation encounters two immediate problems. First, the DMD choice of $\unicode[STIX]{x1D650}_{p}=\text{POD}(\boldsymbol{w}_{0},\ldots ,\boldsymbol{w}_{N-1})$ cannot be made since ${\mathcal{U}}$ may not contain all such data snapshots. Second, and more importantly, since in general $n_{i}>1$ , the modified cost (2.9) is at least of quartic order with respect to $\unicode[STIX]{x1D64E}$ , meaning that an analytical solution is no longer available. To attempt to avoid these problems, consider the related optimisation problem

Now, the variable space of (2.10) is considerably extended with respect to DMD. The low-order model $\hat{\unicode[STIX]{x1D74E}}_{i}\mapsto \unicode[STIX]{x1D647}\unicode[STIX]{x1D648}\unicode[STIX]{x1D647}^{\text{T}}\hat{\unicode[STIX]{x1D74E}}_{i}$ approximating snapshot evolution over one minimal time step $\unicode[STIX]{x1D6FF}t$ now has both its projective component $\unicode[STIX]{x1D647}$ and dynamics matrix $\unicode[STIX]{x1D648}$ as optimisation variables. This is the approach taken in the OMD algorithm (Wynn et al. Reference Wynn, Pearson, Ganapathisubramani and Goulart2013) and avoids the need to calculate POD modes. More importantly, higher powers of the dynamics matrix $\unicode[STIX]{x1D648}$ are avoided by introducing ‘slack’ state variables $\hat{\unicode[STIX]{x1D74E}}_{\boldsymbol{j}}\in \mathbb{R}^{p}$ corresponding to each underlying minimal time step $j\unicode[STIX]{x1D6FF}t$ . The analysis data ensemble ${\mathcal{U}}$ is then employed by enforcing the constraint $\hat{\unicode[STIX]{x1D74E}}_{i}=\boldsymbol{u}_{i},i\in {\mathcal{J}}$ . Finally, again following the OMD formulation, the user-defined parameter $r\leqslant N-1$ specifies the rank of the sought approximating dynamics $\unicode[STIX]{x1D647}\unicode[STIX]{x1D648}\unicode[STIX]{x1D647}^{\text{T}}$ . For completeness, we note that (2.10) reduces to the DMD algorithm in the case that ${\mathcal{W}}={\mathcal{U}},r=N-1$ and $\unicode[STIX]{x1D647}=\text{POD}(\boldsymbol{w}_{0},\ldots ,\boldsymbol{w}_{N-1})$ . The OMD algorithm is recovered in the case that ${\mathcal{W}}={\mathcal{U}}$ .

Figure 3. Schematic representation of a single iteration of the is-OMD algorithm. Given an initial guess for the dynamics $\unicode[STIX]{x1D647}\unicode[STIX]{x1D648}\unicode[STIX]{x1D647}^{\text{T}}$ – represented by solid lines – over each time step and the slack variables (step 1), the slack variables are first updated (step 2) before being fixed to allow an update of $\unicode[STIX]{x1D647}$ and $\unicode[STIX]{x1D648}$ (step 3).

Despite the removal of higher orders of $\unicode[STIX]{x1D648}$ from the cost function, (2.10) cannot be solved directly. Indeed, the inclusion of the slack state variables $\hat{\unicode[STIX]{x1D74E}}_{i}\in \mathbb{R}^{p}$ makes the problem overdetermined, while the products of decision variables – for example, in the $\unicode[STIX]{x1D647}\unicode[STIX]{x1D648}\unicode[STIX]{x1D647}^{\text{T}}\hat{\unicode[STIX]{x1D74E}}_{i}$ term – indicate that the problem is non-convex. To begin to address these difficulties, we propose a two-step iterative solution method indicated schematically in figure 3: first, the model variables $\unicode[STIX]{x1D647}$ and $\unicode[STIX]{x1D648}$ are fixed and the slack variables $\hat{\unicode[STIX]{x1D74E}}_{i}$ are updated by solving a least-squares problem; second, the updated slack state variables are fixed, allowing $\unicode[STIX]{x1D648}$ and $\unicode[STIX]{x1D647}$ to be updated using the standard OMD algorithm applied to the regularly temporally sampled fields $\hat{\unicode[STIX]{x1D74E}}_{i}$ .

The first of these steps – minimising (2.10a ) with respect to $\hat{\unicode[STIX]{x1D74E}}_{i}$ only, subject to constraints (2.10b ), (2.10c ) – is simply a least-squares problem. However, since typically $p=\mathit{O}(10^{5-6})$ , the problem is memory-intensive and likely to be ill-conditioned. Instead, we only seek to minimise the model error over the subspace $\text{Im}(\unicode[STIX]{x1D647})\subset \mathbb{R}^{p}$ , where $\unicode[STIX]{x1D647}$ is fixed within the slack variable update subiteration. This is achieved by considering the projected quadratic cost

where $\boldsymbol{c}_{i}:=\unicode[STIX]{x1D647}^{\text{T}}\hat{\unicode[STIX]{x1D74E}}_{i}\in \mathbb{R}^{r}$ . Since $r\ll p$ , minimising (2.11) with respect to $\boldsymbol{c}_{i}$ is advantageous to minimising (2.10a ) with respect to $\hat{\unicode[STIX]{x1D74E}}_{i}$ . Consequently, the first step of the pairwise optimisation is to solve the constrained least-squares problem

upon which the slack state variables are updated by lifting up via $\hat{\unicode[STIX]{x1D74E}}_{i}\leftarrow \unicode[STIX]{x1D647}\boldsymbol{c}_{i}$ . After this update, the $\hat{\unicode[STIX]{x1D74E}}_{i}$ are fixed and $\unicode[STIX]{x1D647},\unicode[STIX]{x1D648}$ are updated via the OMD algorithm $[\unicode[STIX]{x1D647},\unicode[STIX]{x1D648}]\leftarrow \text{OMD}((\hat{\unicode[STIX]{x1D74E}}_{i})_{i=0}^{N})$ . It is proved in appendix A that this two-step procedure is guaranteed to decrease the cost (2.10a ) which, being bounded below, implies that it will converge asymptotically.

A subtle characteristic of the OMD algorithm (step 6 in algorithm 1) is that the cost function of the minimisation problem (2.13) is invariant under rotations of $\unicode[STIX]{x1D647}$ (Wynn et al. Reference Wynn, Pearson, Ganapathisubramani and Goulart2013). In particular, for any orthogonal matrix $\tilde{\unicode[STIX]{x1D64D}}$ such that $\tilde{\unicode[STIX]{x1D64D}}^{\text{T}}\tilde{\unicode[STIX]{x1D64D}}=\unicode[STIX]{x1D644}$ , the pairs $[\unicode[STIX]{x1D647},\unicode[STIX]{x1D648}]$ and $[\unicode[STIX]{x1D647}\tilde{\unicode[STIX]{x1D64D}},\tilde{\unicode[STIX]{x1D64D}}^{\text{T}}\unicode[STIX]{x1D648}\tilde{\unicode[STIX]{x1D64D}}]$ have equivalent costs. The explanation of this behaviour is that OMD identifies a subspace $\text{Im}(\unicode[STIX]{x1D647})\subset \mathbb{R}^{p}$ upon which a low-order representation of the system dynamics can be provided, and that each $\unicode[STIX]{x1D647}\tilde{\unicode[STIX]{x1D64D}}$ is just a member of the same equivalence class describing this subspace. For visualisation purposes, we therefore propose in steps 10–12 of algorithm 1 to choose a rotation of $\unicode[STIX]{x1D647}$ which ranks its columns by their energetic contribution to the known data.

The is-OMD procedure is summarised in algorithm 1 which outputs: (i) estimated intermediate snapshots $\boldsymbol{w}_{i},i\notin {\mathcal{J}}$ ; (ii) a projection matrix $\unicode[STIX]{x1D647}$ and (iii) an approximate dynamics matrix $\unicode[STIX]{x1D648}$ . The appropriate choice of initial projection and dynamics matrices, $\unicode[STIX]{x1D647}_{0}$ , $\unicode[STIX]{x1D648}_{0}$ , will be discussed in detail in § 6.1 where it is demonstrated that a sensible choice is to set the initial dynamics matrix to be a small perturbation of the identity matrix with normal distribution of standard deviation $\unicode[STIX]{x1D70E}$ , $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,\unicode[STIX]{x1D70E}^{2})+\unicode[STIX]{x1D6FF}_{ij}$ , and the projection space to be POD modes of the analysis data ensemble ${\mathcal{U}}$ , $\unicode[STIX]{x1D647}_{0}=\text{POD}({\mathcal{U}})$ . Additionally, the beneficial impact of choosing a large value of $r$ will be established in § 6.2.

A natural variant of algorithm 1 is to use DMD in place of OMD in step 6, in which case $\unicode[STIX]{x1D647}_{k}:=\text{POD}((\boldsymbol{w}_{i}^{k})_{i=0}^{N})$ would be fixed in step 6 within each iteration. Note that in this case, a final rotation of $\unicode[STIX]{x1D647}$ in steps 10–12 is unnecessary. Finally, it should be noted that the notion of dynamic modes has not yet been defined in the context of algorithm 1. This will be discussed subsequently in § 4.1.

3.1 Convexity of the slack variable update

The equality constrains $\boldsymbol{c}_{i}=\unicode[STIX]{x1D647}^{\text{T}}\boldsymbol{u}_{i},i\in {\mathcal{J}}$ of (2.13) represent time steps at which data are available. Without loss of generality, we consider the problem in which ${\mathcal{J}}=\{0,N\}$ , since the general optimisation problem can be broken into a number of independent subproblems of this form. Now, it is not difficult to prove that (2.13) can be written as the unconstrained least-squares problem

where

and $\unicode[STIX]{x1D743}=\left(\begin{array}{@{}c@{}}\boldsymbol{c}_{N-1}^{\text{T}}\cdots \boldsymbol{c}_{1}^{\text{T}}\end{array}\right)^{\text{T}}\in \mathbb{R}^{(N-1)r}$ . Consequently, (2.13) is convex and admits the solution $\unicode[STIX]{x1D743}=(\unicode[STIX]{x1D63C}^{\text{T}}\unicode[STIX]{x1D63C})^{-1}\unicode[STIX]{x1D63C}^{\text{T}}\boldsymbol{b}$ provided the matrix $\unicode[STIX]{x1D63C}$ is full rank. This is true, and follows from the easily shown fact that $\unicode[STIX]{x1D63C}\unicode[STIX]{x1D743}=0\Leftrightarrow \unicode[STIX]{x1D743}=0$ . Finally, we note that solving a series of independent subproblems has significant advantages in terms of computational memory savings and improved numerical stability due to a reduced condition number of $(\unicode[STIX]{x1D63C}^{\text{T}}\unicode[STIX]{x1D63C})$ compared to the case where $\unicode[STIX]{x1D743}$ and $\boldsymbol{b}$ contain $\unicode[STIX]{x1D647}^{\text{T}}\boldsymbol{u}_{i}$ and $\boldsymbol{c}_{i}$ for all $i\in {\mathcal{J}}$ .

3.2 Computational complexity

The computational cost of the algorithm 1 consists of that associated with the two optimisation subproblems; the least-squares problem (2.13) and the modal optimisation (2.14) where dynamic model is updated via OMD (or DMD). Solving the least-squares problem using, for example, the Moore–Penrose pseudoinverse of $\unicode[STIX]{x1D63C}\in \mathbb{R}^{Nr\times (N-1)r}$ has complexity $\mathit{O}(N^{3}r^{3})$ . Moreover, as discussed in § 3.1 the full least-squares problem can be broken down into a number of smaller, independent, subproblems corresponding to $N_{i}$ intermediate time steps, each of which can be solved at a low computation burden. For example, $(N_{i}=10,r=40)$ and $(N_{i}=20,r=60)$ can be solved in $0.01~\text{s}$ and $0.3~\text{s}$ on a standard desktop computer, respectively, and each subproblem could be parallelised, if required.

For the modal optimisation subproblem (2.14), a discussion of the computational performance of OMD and DMD can be found in (Wynn et al. Reference Wynn, Pearson, Ganapathisubramani and Goulart2013). Both the total number of slack variables $N$ and the approximation rank $r$ affect computation time in addition to the underlying dynamic characteristics of the specific system to which the each algorithm is applied. With respect to the examples considered subsequently: for the cylinder flow in § 6, the is-OMD algorithm converges with a tolerance of $10^{-2}$ after $80$ iterations of algorithm 1, with each call of OMD in subproblem (2.14) requiring $8.0~\text{s}~(N=500,r=50)$ , giving a total computation time for is-OMD of $640~\text{s}$ ; for the application to PIV snapshots of turbulent flow past an axisymmetric bluff body in § 7, is-OMD converges to the same tolerance in only $10$ iterations but with a higher cost of $13.8~\text{s}~(N=120,r=30)$ for each OMD subproblem, resulting in a total computation time of $138~\text{s}$ .

Finally, we note that since both OMD and algorithm 1 are iterative, a possible method to improve algorithmic efficiency would be to apply a (small) fixed number of OMD iterates at each subproblem (2.14). Alternatively, one may apply DMD in place of OMD in subproblem (2.14) to reduce the computational cost. This will be explored in future work. All computations were performed using MATLAB on a standard desktop PC with a 3.4 GHz quad-core Intel i7 processor and 16 GB RAM.

4.1 Dynamic mode extraction

Given a data ensemble $(\boldsymbol{w}_{i})_{i=0}^{N}$ , a typical description (Rowley et al. Reference Rowley, Mezic, Bagheri, Schlatter and Henningson2009; Schmid Reference Schmid2010; Chen et al.  Reference Chen, Tu and Rowley2012) of dynamic modes $\unicode[STIX]{x1D731}_{i}$ and eigenvalues $\unicode[STIX]{x1D706}_{i}$ is that they describe the evolution of the first $N-1$ snapshots according to

where $\unicode[STIX]{x1D652}=[\boldsymbol{w}_{0},\ldots ,\boldsymbol{w}_{N-1}],\unicode[STIX]{x1D731}=[\unicode[STIX]{x1D731}_{1},\ldots ,\unicode[STIX]{x1D731}_{N}]$ and $\unicode[STIX]{x1D651}$ is the Vandermonde matrix

In particular, for $\unicode[STIX]{x1D651}$ constructed using DMD eigenvalues, DMD modes can be interpreted as arising from the solution to the minimisation problem

In view of the observation that accurate spectral information may be identified by algorithm 1, we seek to adapt this approach to calculate dynamic modes from the output of algorithm 1. In particular, consider the output $\unicode[STIX]{x1D648}$ with eigenvalues $\unicode[STIX]{x1D706}_{i}$ obtained from a data ensemble with known data only at points ${\mathcal{J}}\subset \{0,\ldots ,N\}$ . From this, construct a reduced Vandermonde matrix $\unicode[STIX]{x1D651}_{{\mathcal{J}}}$ containing $r$ rows and only the columns whose indices are in ${\mathcal{J}}$ . The dynamic modes associated with algorithm 1 are given in terms of the solution to the reduced minimisation problem

This is a least-squares problem with solution, $\unicode[STIX]{x1D731}$ , from which the dynamic is-OMD modes are defined as

It is noted that computation of the dynamic modes through an inversion of $\unicode[STIX]{x1D651}_{{\mathcal{J}}}$ can be restrictive in the sense that it is assumed that temporal evolution corresponding to coherent structures is highly periodic. In applications where this is not case an alternative approach may be required: an example is presented in § 7 where dynamic modes are obtained by introducing rate constraints to the low-dimensional dynamics.

A technical consideration is that an accurate solution to the problem (4.8) requires $\unicode[STIX]{x1D651}$ to be full rank. However, it is known that Vandermonde matrices are typically ill-conditioned (Williams Reference Williams2011). For this reason, it is also desirable to remove rows of the Vandermonde matrix corresponding to eigenvalues which are of comparative unimportance to the system dynamics. This can be achieved by taking a QR decomposition with column pivoting $\unicode[STIX]{x1D651}_{{\mathcal{J}}}^{\text{T}}=\unicode[STIX]{x1D64C}\unicode[STIX]{x1D64D}\tilde{\unicode[STIX]{x1D64B}}^{\text{T}}$ , where $\unicode[STIX]{x1D64C}^{\mathsf{T}}\unicode[STIX]{x1D64C}=\unicode[STIX]{x1D644}$ , $\unicode[STIX]{x1D64D}$ is an upper triangular matrix and $\tilde{\unicode[STIX]{x1D64B}}$ is a column permutation matrix such that absolute diagonal values of $\unicode[STIX]{x1D64D}$ , $|r_{ii}|$ , are decreasing. A tolerance, $0<\tilde{t}<1$ , is chosen, and the number or rows retained, $n$ , is set to $n=\max \{i:|r_{ii}|>\tilde{t}|r_{11}|\}$ . The intention is that the condition number of the reduced matrix is decreased since rows corresponding to dynamically unimportant eigenvalues (which typically possess a large negative real part) are discarded. The number of removed rows will depend on the tolerance $\tilde{t}$ , which has to be set such that majority of dynamically irrelevant eigenvalues are discarded but all of the dynamically relevant features are retained. An appropriate way to select $\tilde{t}$ is outlined in § 6.2, and the post-processing step is summarised in algorithm 2.

Using the eigenvalues obtained previously, the is-OMD modes of the system (4.1) are obtained from (4.8) for a Vandermonde matrix conditioned with $\tilde{t}=0.01$ . Figure 4 shows modes corresponding to identified eigenvalues, which accurately match the prescribed spatial wavenumbers $k_{1}$ and $k_{2}$ . Note the difference between the columns of $\unicode[STIX]{x1D647}$ in figure 4, where there is a visible interaction between both wavenumbers, a phenomenon which is not evident in $\unicode[STIX]{x1D731}^{is\text{-}OMD}$ . Although $\unicode[STIX]{x1D647}$ is not used in the calculation of $\unicode[STIX]{x1D731}^{is\text{-}OMD}$ , its inclusion in the variable space of algorithm 1 allows for greater freedom in the estimation, which may lead to more accurate $\unicode[STIX]{x1D706}_{i}$ and, hence, more accurate dynamical modes.

4.2 Intermediate snapshot reconstruction

Despite the fact that the intermediate snapshot variables $w_{i}$ in algorithm 1 provide the freedom with which accurate spectral information can be obtained, these variables typically contain high-frequency content not present in the reference data. However, using the eigenvalues $\unicode[STIX]{x1D726}^{is\text{-}OMD}=[\unicode[STIX]{x1D706}_{1},\ldots ,\unicode[STIX]{x1D706}_{n}]$ and dynamic modes $\unicode[STIX]{x1D731}^{is\text{-}OMD}=[\unicode[STIX]{x1D731}_{1},\ldots ,\unicode[STIX]{x1D731}_{n}]$ obtained from algorithm 2, it is possible to obtain an improved approximation of intermediate snapshots corresponding to time steps $i\notin {\mathcal{J}}$ .

To do this, consider the interpretation of the modal decomposition output described through (4.5). At each of the known analysis data snapshots $(\boldsymbol{u}_{k})_{k\in {\mathcal{J}}}$ we consider the projection $(\unicode[STIX]{x1D731}^{is\text{-}OMD})^{\text{T}}\boldsymbol{u}_{k}=:(a_{j})_{j=1}^{r}\in \mathbb{R}^{r}$ and subsequently extrapolate via

where $n_{k}$ is the number of minimal time steps $\unicode[STIX]{x1D6FF}t$ between successive snapshots $\boldsymbol{u}_{k}$ and $\boldsymbol{u}_{k+1}$ .

The above technique is applied to the system (4.1) with the same parameters as in § 4.1, with eigenvalues $\unicode[STIX]{x1D706}_{j}$ and dynamic modes $\unicode[STIX]{x1D731}_{j}$ obtained with algorithm 2, to produce intermediate snapshots $\tilde{\boldsymbol{w}}_{i}$ . Figure 5 presents the temporal evolution of the snapshot error $\unicode[STIX]{x1D716}_{i}=\Vert \tilde{\boldsymbol{w}}_{i}-\boldsymbol{w}_{i}^{ref}\Vert ^{2}/\Vert \boldsymbol{w}_{i}^{ref}\Vert ^{2}$ , where it can be seen that due to the linear nature of the system, the error increases linearly when $\tilde{\boldsymbol{w}}_{i}$ is estimated further away from known snapshots $\boldsymbol{u}_{i}$ . However, since the eigenvalues $\unicode[STIX]{x1D706}$ are estimated accurately, snapshot extrapolation gives a small error $\unicode[STIX]{x1D716}_{i}<10^{-4}$ .

Figure 5. Temporal evolution of the snapshots reconstruction error $\unicode[STIX]{x1D716}_{i}=\Vert \tilde{\boldsymbol{w}}_{i}-\boldsymbol{w}_{i}^{ref}\Vert ^{2}/\Vert \boldsymbol{w}_{i}^{ref}\Vert ^{2}$ , where $\tilde{\boldsymbol{w}}_{i}$ is defined by (4.10).

5.1 Robustness of CS-DMD and is-OMD

We consider data sampled from (5.1) in the case of two frequencies $\unicode[STIX]{x1D714}_{1}=\unicode[STIX]{x03C0}/20,\unicode[STIX]{x1D714}_{2}=2\unicode[STIX]{x03C0}/25$ , with respective amplitudes $A_{1}=1,A_{2}=0.5$ , and minimal time step $\unicode[STIX]{x1D6FF}t=1$ . The analysis data ensemble ${\mathcal{U}}$ is selected with an average sampling time step satisfying

as in § 4.

The first step of CS-DMD is to apply compressive sampling to ${\mathcal{U}}$ to form an approximation $\hat{{\mathcal{W}}}$ to the fully resolved data ensemble ${\mathcal{W}}$ . Recall from § 2.2 that this is achieved by (i) choosing and fixing a temporal basis $\unicode[STIX]{x1D729}$ , then (ii) successively adding a number, $k$ say, of elements of this basis which best correlate with the available data. Motivated by the fact that the underlying data sampled from (5.1) are sparse in the Fourier basis, we set $\unicode[STIX]{x1D729}$ to be the inverse Fourier Transform and obtain approximations $\hat{{\mathcal{W}}}_{k}\approx {\mathcal{W}}$ corresponding to extracting $k=4,8$ and $20$ dominant basis vectors. DMD is then applied to each $\hat{{\mathcal{W}}}_{k}$ to determine the identified eigenvalue information.

Since we do not want to assume a priori the number of dynamically important structures present in a given data set, it is desirable that the accuracy of the frequency information provided by CS-DMD behaves predictably with the requested number of basis elements $k$ . Figure 6 indicates that this is not the case. Indeed, the frequencies $\unicode[STIX]{x1D714}_{i}$ are accurately identified only in the case $k=8$ , with $k=4$ failing to identify the higher frequency $\unicode[STIX]{x1D714}_{2}$ , while the choice $k=20$ results in an underapproximation of $\unicode[STIX]{x1D714}_{1}$ .

For $k=4$ , inaccuracy arises from the discrete Fourier frequencies implied by the chosen basis $\unicode[STIX]{x1D729}$ . Since the fully resolved data ensemble contains $900$ samples, the chosen basis contains only discrete frequencies $\unicode[STIX]{x1D714}_{F}=(2\unicode[STIX]{x03C0}n/900)_{n=0}^{900}$ , $k$ of which are used to express the data. For this particular example, no single $\unicode[STIX]{x1D714}_{F}$ coincides with $\unicode[STIX]{x1D714}_{1}=\unicode[STIX]{x03C0}/20$ . Instead there are multiple values of $\unicode[STIX]{x1D714}_{F}$ in a small neighbourhood of $\unicode[STIX]{x1D714}_{1}$ and, since $A_{1}>A_{2}$ , the CoSamp algorithm applied with $k=4$ (incorrectly) identifies a pair of frequencies $\unicode[STIX]{x1D714}_{F}$ close to $\unicode[STIX]{x1D714}_{1}$ . The result is improved for $k=8$ , which is sufficiently large to resolve both $\unicode[STIX]{x1D714}_{1}$ and the higher frequency $\unicode[STIX]{x1D714}_{2}$ . However, for $k=20$ it is shown in figure 6(d) that compressive sampling identifies Fourier frequencies $\unicode[STIX]{x1D714}_{F}$ not present in the underlying system, in addition to those close to the true values $\unicode[STIX]{x1D714}/\unicode[STIX]{x03C0}=0.05,0.08$ . Upon application of DMD to ${\mathcal{W}}_{k}$ , these extra frequencies result in an incorrect identification of $\unicode[STIX]{x1D714}_{1}$ .

Figure 6. The DMD eigenvalue lattice obtained using snapshots reconstructed with CS-DMD for different numbers of sparse structures (a–c), with $\unicode[STIX]{x1D706}_{is\text{-}OMD}$ , is given for $r=45$ . The Fourier spectrum obtained by compressive sampling for $k=20$ is shown in (d).

Such a lack of robustness with respect to $k$ is not evident in the application of algorithm 1 to this example, which, as can be seen in figure 6(a–c), correctly identifies the two underlying frequencies in the data using a model of order $r=45$ . The is-OMD results in figure 6(a–c) present only four eigenvalues of interest. The eigenvalues of relevance were chosen based on the energetic criterion similar to approaches in Rowley et al. (Reference Rowley, Mezic, Bagheri, Schlatter and Henningson2009), Chen et al.  (Reference Chen, Tu and Rowley2012), Jovanovic et al. (Reference Jovanovic, Schmid and Nichols2014), where the magnitudes of the DMD modes are examined. Here, the magnitude of a solution to (4.8), $\Vert \unicode[STIX]{x1D731}_{k}\Vert _{2}$ , is assessed and eigenvalues with the largest magnitude of the associated modes are selected. Note that, if $\unicode[STIX]{x1D651}_{{\mathcal{J}}}$ contains dynamically unimportant eigenvalues with large decay rate, the associated modes will have an excessively large magnitude in order to compensate for the low value of $\unicode[STIX]{x1D706}_{j}^{i-1}$ at later time steps. Therefore, in order to obtain a meaningful ranking of modes based on the energy criterion, it is important to remove dynamically unimportant rows of $\unicode[STIX]{x1D651}_{{\mathcal{J}}}$ using algorithm 2. Here, (4.8) is solved using $\unicode[STIX]{x1D651}_{{\mathcal{J}}}$ conditioned with $\tilde{t}=10^{-3}$ retaining nine rows of Vandermonde matrix, and consequently nine structures. Figure 7 shows a relative energetic contribution of each mode to the system dynamics defined as $|X_{k}|=\Vert \unicode[STIX]{x1D731}_{k}\Vert _{2}/\!\sum _{j}\Vert \unicode[STIX]{x1D731}_{k}\Vert _{2}$ , demonstrating a clearly visible drop in $\Vert \tilde{\unicode[STIX]{x1D731}}_{k}\Vert _{2}$ between dynamically important and unimportant eigenvalues and allowing the identification of frequencies presented in figure 6(a–c).

Figure 7. The relative contribution of the is-OMD modes.

Note that another variable prescribed a priori in the CS-DMD methodology is the rank $r$ of the DMD approximation, which is upper bounded by the number of extracted basis vectors $k$ . The CS-DMD results presented in figure 6 were obtained with DMD approximation of rank $r=4$ as opposed to $r=45$ of the is-OMD results. The reason for selecting this rank for the CS-DMD approximation can be explained if the maximal case $r=k$ is instead considered. With this choice, DMD eigenvalue information will replicate the Fourier frequencies which most accurately correlate with the analysis data ensemble, ${\mathcal{U}}$ . Figure 8(a) presents the relative contribution of each mode for $r=k=20$ . Although the frequencies corresponding to the most dominant modes are close to the true values, it is nonetheless not clear from figure 8(a) how to choose a threshold which will allow frequencies of dynamical importance to be retained. A similar situation occurs in figure 8(c) for $r=k=8$ .

For $r=k=8$ eigenvalues coincide with $\unicode[STIX]{x1D714}_{F}$ which lie in a small neighbourhood of $\unicode[STIX]{x1D714}_{1}$ and $\unicode[STIX]{x1D714}_{2}$ with a small error $\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D706}}$ corresponding to those frequencies. However, it is shown in figure 6(b) that if a combination $k=8,r=4$ is used, $\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D706}}$ decreases further. Based on these observations, in § 5.2 where a statistical sample of sinusoidal flows is investigated, for CS-DMD methodology a combination of $k=8$ and $r=4$ is utilised.

Figure 8. The output of the CS-DMD methodology for different values of $k$ and $r$ where $k=r$ . The importance of modes and corresponding eigenvalue spectrum for $k=r=20$ is shown in (a,b) while those corresponding to $k=r=8$ are presented in (c,d).

5.2 Frequency estimation analysis: statistical comparison between compressive sampling and is-OMD

To study the differences in performance of is-OMD and compressive sampling, both methodologies were applied to the system (5.1) for a range of frequencies and average sampling rates. In particular, the lower frequency is fixed, as above, to $\unicode[STIX]{x1D714}_{1}=\unicode[STIX]{x03C0}/20$ , while the higher system frequency takes one of values

Subsequently, for each of value of $\unicode[STIX]{x1D714}_{2}(n)$ a number of downsampled analysis data ensembles ${\mathcal{U}}_{nm}$ are constructed whose average sampling rates satisfy

that is, each exceeds the Nyquist criterion to differing degrees. This is achieved by constructing fully resolved data sets

and sampling non-uniform analysis data ensembles ${\mathcal{U}}_{nm}\subset {\mathcal{W}}_{nm}$ , each of cardinality $|{\mathcal{U}}_{nm}|=45$ . Note that the fully resolved data set is varied for each pair $(n,m)$ in order that analysis data sets with different average downsampling rates (as a proportion of $\unicode[STIX]{x1D6FF}t_{2}^{\ast }$ ) can be constructed to possess the same proportion, 5 %, of elements to the fully resolved ensemble.

The CS-DMD and is-OMD algorithms are applied to each data set ${\mathcal{U}}_{nm}$ . Motivated by § 5, we apply the CoSamp algorithm with sparse basis matrix $\unicode[STIX]{x1D729}$ as the inverse Fourier transform and set the sparsity level to $k=8$ . Algorithm 1 is initialised with $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,\unicode[STIX]{x1D70E}^{2})+\unicode[STIX]{x1D6FF}_{ij}$ , that is, a small perturbation of the identity matrix with $\unicode[STIX]{x1D70E}=\sqrt{0.3}$ , and $\unicode[STIX]{x1D647}_{0}=\text{POD}({\mathcal{U}}_{nm})$ , with the modelling dimension set as $r=|{\mathcal{U}}_{nm}|=45$ .

Figures 9(a) and 9(b) show the proportional frequency error $\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D706}}$ corresponding to $\unicode[STIX]{x1D714}_{1},\unicode[STIX]{x1D714}_{2}$ , respectively, averaged over the different values of $\unicode[STIX]{x0394}t_{av}({\mathcal{U}}_{nm})/\unicode[STIX]{x1D6FF}t_{2}^{\ast }$ . Note that, since the data are sampled in such a way to achieve a particular value of $\unicode[STIX]{x0394}t_{av}/\unicode[STIX]{x1D6FF}t_{2}^{\ast }$ , the corresponding downsampling ratio for $\unicode[STIX]{x1D6FF}t_{1}^{\ast }$ is $\unicode[STIX]{x0394}t_{av}/\unicode[STIX]{x1D6FF}t_{1}^{\ast }=(\unicode[STIX]{x0394}t_{av}/\unicode[STIX]{x1D6FF}t_{2}^{\ast })(\unicode[STIX]{x1D714}_{1}/\unicode[STIX]{x1D714}_{2})$ , meaning that $\unicode[STIX]{x0394}t_{av}/\unicode[STIX]{x1D6FF}t_{1}^{\ast }$ are not similarly grouped. Consequently, a discrete set of values is defined, ${\mathcal{S}}=\{1.02,1.04,\ldots ,1.92\}$ , and for each element ${\mathcal{S}}_{i}$ results satisfying $|\unicode[STIX]{x0394}t_{av}/\unicode[STIX]{x1D6FF}t_{1}^{\ast }-{\mathcal{S}}_{i}|<0.01$ are grouped together and averaged accordingly. It is clear from figure 9(a,b) that the proportional frequency error is reduced by application of algorithm 1 in comparison to CS-DMD.

Figure 9. Average proportional frequency error $\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D706}}$ corresponding to $\unicode[STIX]{x1D714}_{1}$ (a) and $\unicode[STIX]{x1D714}_{2}$ (b) for each $\unicode[STIX]{x0394}t_{av}/\unicode[STIX]{x1D6FF}_{2}^{\ast }$ ; and corresponding to each $\unicode[STIX]{x1D714}_{2}$ when $\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D706}}$ is averaged over a range of different downsampling ratios $\unicode[STIX]{x0394}t_{av}/\unicode[STIX]{x1D6FF}_{2}^{\ast }$ (c).

Figure 9(c) shows the proportional frequency error $\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D706}}$ corresponding to $\unicode[STIX]{x1D714}_{2}$ , as $\unicode[STIX]{x1D714}_{2}$ is varied, each averaged over the $55$ different values of the downsampling ratio $\unicode[STIX]{x0394}t_{av}/\unicode[STIX]{x1D6FF}t_{2}^{\ast }$ . The CS-DMD error is approximately constant, while to is-OMD error decays as $|\unicode[STIX]{x1D714}_{1}-\unicode[STIX]{x1D714}_{2}|$ increases. It should be noted that such a pattern can be observed if the OMD algorithm is applied to fully time-resolved snapshots ${\mathcal{W}}$ which contain observation noise. This is, pairs of eigenvalues satisfying $|\text{Im}(\unicode[STIX]{x1D706}_{i})-\text{Im}(\unicode[STIX]{x1D706}_{j})|\ll 1$ are sensitive to observation noise.

6.1 Initial condition analysis

To assess the most appropriate properties of $\unicode[STIX]{x1D647}_{0}$ and $\unicode[STIX]{x1D648}_{0}$ to initialise is-OMD, three classes of initial condition are considered:

1. (i) $\unicode[STIX]{x1D647}_{0}$ is set to POD modes of the known data, $\unicode[STIX]{x1D650}$ , and an identity matrix chosen to approximate $\unicode[STIX]{x1D648}_{0}$ , i.e. $\unicode[STIX]{x1D647}_{0}=\text{POD}(\unicode[STIX]{x1D650})$ , $\unicode[STIX]{x1D648}_{0}=\unicode[STIX]{x1D644}_{r}$ ;

2. (ii) $\unicode[STIX]{x1D647}_{0}=\text{POD}(\unicode[STIX]{x1D650})$ and an identity matrix with entries perturbed by noise used for $\unicode[STIX]{x1D648}_{0}$ : $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,\unicode[STIX]{x1D70E})+\unicode[STIX]{x1D6FF}_{ij}$ ;

3. (iii) OMD is applied to data undersampled with a constant time step $\unicode[STIX]{x0394}t$ , $\overline{\unicode[STIX]{x1D650}}$ , to obtain $\unicode[STIX]{x1D647}_{0}$ and $\unicode[STIX]{x1D648}_{0}$ , i.e. $[\unicode[STIX]{x1D648}_{0},\unicode[STIX]{x1D647}_{0}]=\text{OMD}(\overline{\unicode[STIX]{x1D650}})$ .

The motivation for $[\unicode[STIX]{x1D648}_{0},\unicode[STIX]{x1D647}_{0}]=\text{OMD}(\overline{\unicode[STIX]{x1D650}})$ is that in systems exhibiting limit cycle behaviour, their dynamics does not qualitatively change with time. In such a case, two sets of data can be acquired, one with non-uniform $\unicode[STIX]{x0394}t$ which is used as an input to is-OMD, $\unicode[STIX]{x1D650}$ , and a second one which is uniformly subsampled, $\overline{\unicode[STIX]{x1D650}}$ . In such a case $\overline{\unicode[STIX]{x1D650}}$ is not sufficient to obtain all the dynamics of interest; however, the output of OMD can still be used for $\unicode[STIX]{x1D647}_{0}$ and $\unicode[STIX]{x1D648}_{0}$ .

In order to consider the role of the initial conditions, the same downsampling strategy and approximation rank $r$ are used for all three cases. In particular, ${\mathcal{U}}$ is sampled non-uniformly, with $\unicode[STIX]{x0394}t_{av}=10\unicode[STIX]{x1D6FF}t$ and without constraints on $\unicode[STIX]{x0394}t_{min}$ and $\unicode[STIX]{x0394}t_{max}$ , while the projection rank is set to $r=75$ . The dimensions of both data sets ${\mathcal{W}}$ , ${\mathcal{U}}$ are $\unicode[STIX]{x1D652}\in \mathbb{R}^{p\times 750}$ , $\unicode[STIX]{x1D650}\in \mathbb{R}^{p\times 75}$ , i.e. $|{\mathcal{I}}|=750$ and $|{\mathcal{J}}|=75$ . The $\overline{\unicode[STIX]{x1D650}}\in \mathbb{R}^{p\times 75}$ was sampled such that $\unicode[STIX]{x0394}t_{av}=10\unicode[STIX]{x1D6FF}t$ , which is sufficient to resolve a time scale of $2/3T$ , where $T$ is a shedding frequency. Note that cylinder flow contains harmonics with time scales of $T/n$ for $n\in \mathbb{Z}^{+}$ , meaning that $2/3T$ is not sufficient to resolve the subharmonic with a time scale of $T/2$ .

The extracted dynamical information for $\unicode[STIX]{x1D648}_{0}=\unicode[STIX]{x1D644}_{r}$ , $[\unicode[STIX]{x1D648}_{0},\unicode[STIX]{x1D647}_{0}]=\text{OMD}(\overline{\unicode[STIX]{x1D650}})$ and $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,\unicode[STIX]{x1D70E})+\unicode[STIX]{x1D6FF}_{ij}$ with $\unicode[STIX]{x1D70E}=10^{-2},10^{-3}$ are presented in figure 10. In all of the analysed cases, only the eigenvalues retained in algorithm 2 for $\tilde{t}=0.3$ are presented. The Strouhal number is defined as $St=fD/U_{\infty }$ , where $D$ is the cylinder diameter and $U_{\infty }$ is the inflow velocity. Assuming that flow field was non-dimensionalised using a cylinder diameter and an inflow velocity, the Strouhal number can be extracted from the phase information of each eigenvalue, $St=\text{Im}(\unicode[STIX]{x1D706})/(2\unicode[STIX]{x03C0})$ . The magnitude of the eigenvalue provides the information about the growth rate, which is expressed in such a way so that growth rate, $\unicode[STIX]{x1D6FD}$ , follows $\unicode[STIX]{x1D6FD}^{t}$ , i.e. $\unicode[STIX]{x1D6FD}=\exp (\text{Re}(\unicode[STIX]{x1D706}))$ . The reference eigenvalues in figure 10 were obtained by applying the OMD algorithm to $\unicode[STIX]{x1D652}$ for $r=30$ , and consist of the asymptotic eigenvalues representing the limit cycle behaviour and eigenvalues at $\unicode[STIX]{x1D6FD}=0.9$ characterising relaxation of the flow onto its limit cycle (Bagheri Reference Bagheri2013). The primary objective is to assess the accuracy at which the is-OMD algorithm approximates the asymptotic eigenvalues, since the values at $\unicode[STIX]{x1D6FD}=0.9$ correspond to dynamics at extremely low energy for the chosen data set $\unicode[STIX]{x1D652}$ .

Figure 10. Eigenvalues of the dynamic matrix $\unicode[STIX]{x1D648}$ obtained using the is-OMD algorithm for different initial conditions (a). The error in eigenvalue magnitude for identity matrices with $\unicode[STIX]{x1D748}\in [10^{-9},10^{-2}]$ (b), obtained for the projection rank $r=75$ .

In the case where $\unicode[STIX]{x1D648}_{0}=\unicode[STIX]{x1D644}_{r}$ , a majority of the eigenvalues of $\unicode[STIX]{x1D648}$ are characterised by $\text{Re}(\unicode[STIX]{x1D706})\ll 0$ and are discarded in algorithm 2 due to the choice of $\tilde{t}$ . None of the frequencies present in the system are recovered. For $[\unicode[STIX]{x1D648}_{0},\unicode[STIX]{x1D647}_{0}]=\text{OMD}(\overline{\unicode[STIX]{x1D650}})$ , only frequencies within time-scale range of $\overline{\unicode[STIX]{x1D650}}$ are correctly estimated (i.e. $St\in [-0.2,0.2]$ ). Figure 10 demonstrates that noise must be added to $\unicode[STIX]{x1D648}_{0}$ in order to extract high-frequency harmonics. It is interesting to note that as the standard deviation increases, more frequency information is recovered. In particular, for an order of magnitude increase in $\unicode[STIX]{x1D70E}$ , an additional conjugate pair of eigenvalues is correctly identified and the growth rate of all eigenvalues, $\unicode[STIX]{x1D6FD}$ , is more accurate.

In order to further demonstrate a beneficial influence of noise addition, a repeated analysis was performed for $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,\unicode[STIX]{x1D70E})+\unicode[STIX]{x1D6FF}_{ij}$ with $\unicode[STIX]{x1D70E}\in [10^{-9},10^{-2}]$ . Accuracy in the estimation of seven most dominant eigenvalues is analysed and the proportional error is defined as $\unicode[STIX]{x1D716}:=\sum _{i=1}^{7}|\unicode[STIX]{x1D706}_{i}^{true}-\unicode[STIX]{x1D706}_{i}^{is\text{-}OMD}|/\sum _{i=1}^{7}|\unicode[STIX]{x1D706}_{i}^{true}|$ . Figure 10 demonstrates that $\unicode[STIX]{x1D716}$ decreases as $\unicode[STIX]{x1D70E}$ increases. It is clear that, in order for the is-OMD algorithm to successfully recover dynamical information, a certain degree of flexibility has to be included in $\unicode[STIX]{x1D648}_{0}$ . For $\unicode[STIX]{x1D648}_{0}=\unicode[STIX]{x1D644}_{r}$ and $[\unicode[STIX]{x1D648}_{0},\unicode[STIX]{x1D647}_{0}]=\text{OMD}(\overline{\unicode[STIX]{x1D650}})$ , the dynamics corresponding to dominant structures present in the system, i.e. constant mean mode and shedding vortices, are strongly embedded in the initial matrix structure. In such cases the objective function can be significantly reduced in few iterations, indicating that such a $\unicode[STIX]{x1D648}_{0}$ predetermines a local minimum of (2.10). This problem appears to be avoided simply by randomly perturbing the initial matrices. Note that it is possible to adjust a frequency range of the eigenvalue spectrum of $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,\unicode[STIX]{x1D70E})+\unicode[STIX]{x1D6FF}_{ij}$ by adjusting a value of $\unicode[STIX]{x1D70E}$ , where larger value of $\unicode[STIX]{x1D70E}$ will cover larger range of frequencies. If the system exhibits primarily low-frequency dynamics it is desirable to set $\unicode[STIX]{x1D70E}$ to a lower value such that an eigenvalue spectrum of $\unicode[STIX]{x1D648}_{0}$ is in a neighbourhood of the expected range of the frequencies of interest.

6.2 Rank size analysis

It was demonstrated in § 5.1 that setting a high value of $k$ in the CS-DMD approach induces Fourier modes corresponding to high-frequency oscillations not present in time-resolved data. It is therefore important to consider the impact of the equivalent parameter in the is-OMD algorithm, that is, the dimension $r$ of $\text{Im}(\unicode[STIX]{x1D647})$ . It will be demonstrated here that larger $r$ improves accuracy in the recovered eigenvalues, which consequently leads to an improved accuracy of $\unicode[STIX]{x1D731}^{is\text{-}OMD}$ in algorithm 2. An analysis in terms of the eigenvalue and mode estimation is performed for $r=[25,50,75]$ using the same sampling strategy as in § 6.1 and setting $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,0.3)+\unicode[STIX]{x1D6FF}_{ij}$ , $\unicode[STIX]{x1D647}_{0}=\text{POD}(\unicode[STIX]{x1D650})$ .

Figure 11 presents eigenvalue lattices for different values of $r$ and demonstrates a positive impact of increasing $r$ on the accuracy of the dynamic estimation. When $r$ increases, a progressively greater number of asymptotic eigenvalues is recovered and their growth rates $\unicode[STIX]{x1D6FD}$ converge towards the correct value of 1. The number of modes which can be calculated is proportional to the number of recovered eigenvalues. For $r$ of 25, 50 and 75 there are respectively one, three and four conjugate pairs of eigenvalues that are estimated with small error, and with growth rate $\unicode[STIX]{x1D6FD}>0.95$ . The dynamic modes are estimated as a solution to (4.8) which in turn depends upon $\unicode[STIX]{x1D651}_{{\mathcal{J}}}$ being constructed using accurate eigenvalues. Figure 13 shows dynamic modes corresponding to eigenvalues with $\unicode[STIX]{x1D6FD}>0.95$ obtained for $r=75$ , and it demonstrates that the mode shapes closely resemble the coherent structures obtained from time-resolved data (figure 12). The dynamic modes of similar precision can be obtained for other eigenvalues presented in 11, where $\unicode[STIX]{x1D6FD}>0.95$ , therefore mode shapes for $r=25$ and $r=50$ are omitted here.

Note that for $r=25$ and $r=75$ a pair of eigenvalues is recovered which contains an error in $\unicode[STIX]{x1D6FD}$ . Figure 14 shows the corresponding dynamic modes. Although these structures are not as coherent as for eigenvalues where $\unicode[STIX]{x1D6FD}>0.95$ , there is nonetheless a resemblance to the structures present in the flow (figure 12). It is expected that when $r$ is increased even further the eigenvalue accuracy improves, leading to better precision in the estimation of the dynamic modes. In practice, when $\unicode[STIX]{x1D647}_{0}=POD(\unicode[STIX]{x1D650})$ and $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,\unicode[STIX]{x1D70E})+\unicode[STIX]{x1D6FF}_{ij}$ are used, $r$ is only limited by $|{\mathcal{J}}|$ . This quantity is usually much higher than for the results presented here, and the choice of $r$ would be limited only by the processing cost.

Figure 11. Eigenvalues of the dynamic matrix $\unicode[STIX]{x1D648}$ for $r=[25,50,75]$ (a). The error $\unicode[STIX]{x1D716}_{r}=(1/|{\mathcal{I}}|)\sum _{i\in {\mathcal{I}}}\Vert \tilde{\unicode[STIX]{x1D652}}-\unicode[STIX]{x1D652}^{ref}\Vert ^{2}/\Vert \unicode[STIX]{x1D652}^{ref}\Vert ^{2}$ for $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,0.3)+\unicode[STIX]{x1D6FF}_{ij}$ with different $r$ (b).

Figure 12. Modes $\unicode[STIX]{x1D731}=\unicode[STIX]{x1D647}\unicode[STIX]{x1D64B}$ obtained by applying the OMD algorithm to a time-resolved cylinder flow data.

Figure 13. Modes $\unicode[STIX]{x1D731}^{is\text{-}OMD}$ corresponding to eigenvalues with $\unicode[STIX]{x1D6FD}>0.95$ obtained using the is-OMD algorithm for $r=75$ .

Figure 14. Modes $\unicode[STIX]{x1D731}^{is\text{-}OMD}$ corresponding to eigenvalues presented in figure 11 with $\unicode[STIX]{x1D6FD}<0.95$ ; $St=0.28$ , $\unicode[STIX]{x1D6FD}=0.91$ for $r=25$ (a) and $St=0.69$ , $\unicode[STIX]{x1D6FD}=0.81$ for $r=75$ (b).

When unknown data sets are reconstructed using (4.10), the value of $r$ impacts the accuracy of estimated snapshots, $\tilde{\unicode[STIX]{x1D652}}=[\tilde{\boldsymbol{w}}_{1},\ldots ,\tilde{\boldsymbol{w}}_{N}]$ for $i\notin {\mathcal{J}}$ , through the precision and number of reconstructed eigenvalues $\unicode[STIX]{x1D706}_{j}$ and dynamic modes $\unicode[STIX]{x1D731}_{j}$ , and is expected to improve when $r$ is increased. This fact is confirmed in figure 11, which presents the evolution of the error norm, $\unicode[STIX]{x1D716}_{r}=(1/|{\mathcal{I}}|)\sum _{i\in {\mathcal{I}}}\Vert \tilde{\unicode[STIX]{x1D652}}-\unicode[STIX]{x1D652}^{ref}\Vert ^{2}/\Vert \unicode[STIX]{x1D652}^{ref}\Vert ^{2}$ for $r\in [10,70]$ . There is a visible decay in the value of $\unicode[STIX]{x1D716}_{r}$ , where $\unicode[STIX]{x1D716}_{r}$ is less than 5 % for $r\geqslant 30$ . Note that, despite being governed by nonlinear Navier–Stokes equations, cylinder flow in the limit cycle is known to be well approximated by a linear model consisting of oscillating structures. For more dynamically complex systems, the value of $\unicode[STIX]{x1D716}_{r}$ is expected to have a larger magnitude than values presented in figure 11.

Finally, we note that the accuracy of $\tilde{\unicode[STIX]{x1D652}}$ depends upon the value of $\unicode[STIX]{x1D706}_{j}$ used in (4.10). The number and choice of $\unicode[STIX]{x1D706}_{j}$ is determined based on the number of retained rows in $\unicode[STIX]{x1D651}_{{\mathcal{J}}}$ , which is conditioned with an appropriate value of $\tilde{t}$ . When $\tilde{t}$ is small, a majority of $\unicode[STIX]{x1D706}_{j}$ in $\unicode[STIX]{x1D651}_{{\mathcal{J}}}$ are retained, including dynamically unimportant values, introducing an error in $\tilde{\unicode[STIX]{x1D652}}$ . On the other hand, if $\tilde{t}$ is large, dynamically relevant $\unicode[STIX]{x1D706}_{j}$ are removed. In order to find an appropriate value of $\tilde{t}$ , and consequently number of retained $\unicode[STIX]{x1D706}_{j}$ , let us compare snapshots estimated using (4.10) with reference measurements. Starting an extrapolation at each $i\in {\mathcal{J}}$ a snapshot is estimated at the time step when the next measurements is acquired, i.e. $\tilde{\boldsymbol{w}}_{i+n_{i}}$ , where $n_{i}:=\unicode[STIX]{x0394}t_{i}/\unicode[STIX]{x1D6FF}t$ is the number of minimal time steps $\unicode[STIX]{x1D6FF}t$ between the next element of ${\mathcal{U}}$ . Performing a comparison with all of the available measurements, a proportional error metric $\unicode[STIX]{x1D716}_{\tilde{t}}=(1/|{\mathcal{J}}|)\sum _{i\in {\mathcal{J}}}\Vert \tilde{\boldsymbol{w}}_{i+n_{i}}-\boldsymbol{u}_{i+n_{i}}\Vert /\Vert \boldsymbol{u}_{i+n_{i}}\Vert$ is defined, and $\tilde{t}$ is estimated such that the resulting $\tilde{\boldsymbol{w}}_{i+n_{i}}$ minimises $\unicode[STIX]{x1D716}_{\tilde{t}}$ .

An example for the cylinder flow is-OMD results with $r=70$ is presented in figure 15, which shows $\unicode[STIX]{x1D716}_{\tilde{t}}$ for $\tilde{\boldsymbol{w}}_{i+n_{i}}$ estimated with $\tilde{t}\in [10^{-4},1]$ . For this particular case, $\unicode[STIX]{x1D716}_{\tilde{t}}$ is minimised at $\tilde{t}=0.21$ , where $\tilde{\unicode[STIX]{x1D652}}$ is calculated using the nine most dominant eigenvalues (figure 15). Although $\tilde{t}=0.21$ gives the smallest values of $\unicode[STIX]{x1D716}_{\tilde{t}}$ , there is a clear trough visible for $\tilde{t}\in [0.01,0.75]$ , and setting $\tilde{t}$ to any value in that range will give a good estimation of snapshots at $i\notin {\mathcal{J}}$ . In figure 11, each value of $\unicode[STIX]{x1D716}_{r}$ was obtained for $\tilde{\unicode[STIX]{x1D652}}$ estimated with the smallest value of $\tilde{t}$ .

Figure 15. The error $\unicode[STIX]{x1D716}_{\tilde{t}}=(1/|{\mathcal{J}}|)\sum _{i\in {\mathcal{J}}}\Vert \tilde{\boldsymbol{w}}_{i+n_{i}}-\boldsymbol{u}_{i+n_{i}}\Vert /\Vert \boldsymbol{u}_{i+n_{i}}\Vert$ for different $\tilde{t}$ (a) and retained is-OMD eigenvalue spectrum for $\tilde{t}=0.21$ (b).

6.3 The importance of random sampling

One of the key properties of the compressive sampling methodology, described in § 2.2, is its reliance on non-uniform sampling (Candes & Tao Reference Candes, J. and Tao2006b ). Here, it is demonstrated that is-OMD can also be improved by random sampling. Three different sampling strategies are analysed, with approximately the same $|{\mathcal{J}}|$ and $\unicode[STIX]{x0394}t_{av}$ for different $\unicode[STIX]{x0394}t_{min}$ and $\unicode[STIX]{x0394}t_{max}$ . Depending on $\unicode[STIX]{x0394}t_{min}$ , it is possible to use the Nyquist sampling criterion to estimate the range of different time scales that can be recovered as a multiple of shedding time scale $T$ . Setting $(\unicode[STIX]{x1D648}_{0})_{ij}=N(0,0.3)+\unicode[STIX]{x1D6FF}_{ij}$ , $\unicode[STIX]{x1D647}_{0}=POD(\unicode[STIX]{x1D650})$ and $r=70$ , is-OMD is applied to the following sampling strategies.

1. (i) Uniform sampling: ${\mathcal{U}}$ is sampled uniformly with $\unicode[STIX]{x0394}t=10\unicode[STIX]{x1D6FF}t$ . Such a sampling strategy corresponds to three snapshots per shedding period and is sufficient to resolve time scales greater than $2/3T$ . In particular $|{\mathcal{J}}|=79$ and $|{\mathcal{I}}|=781$ .

2. (ii) Random sampling (large variance): ${\mathcal{U}}$ is sampled non-uniformly such that $\unicode[STIX]{x0394}t_{min}=3\unicode[STIX]{x1D6FF}t$ ( $0.2T$ ) and $\unicode[STIX]{x0394}t_{max}=17\unicode[STIX]{x1D6FF}t$ ( $1.15T$ ), which results in $|{\mathcal{J}}|=80$ and $|{\mathcal{I}}|=783$ . Together $\unicode[STIX]{x0394}t_{min}$ and $\unicode[STIX]{x0394}t_{max}$ encompass 3 % of $|{\mathcal{J}}|$ .

3. (iii) Random sampling (small variance): ${\mathcal{U}}$ is sampled non-uniformly such that $\unicode[STIX]{x0394}t_{min}=6\unicode[STIX]{x1D6FF}t$ ( $0.4T$ ) and $\unicode[STIX]{x0394}t_{max}=14\unicode[STIX]{x1D6FF}t$ ( $0.95T$ ), which results in $|{\mathcal{J}}|=79$ and $|{\mathcal{I}}|=783$ . Together $\unicode[STIX]{x0394}t_{min}$ and $\unicode[STIX]{x0394}t_{max}$ encompass 5 % of $|{\mathcal{J}}|$ .

Figure 16 shows the eigenvalues retained using the algorithm 2 with $\tilde{t}=0.1$ for all three cases. Recall that the limit cycle dynamics of the cylinder flow are characterised by structures oscillating with frequency $n/T$ , where $n\in \mathbb{Z}^{+}$ , and having corresponding time scales of $T/n$ . For the cylinder data used at in the analysis, a uniform time step $\unicode[STIX]{x0394}t=10\unicode[STIX]{x1D6FF}t$ is high enough to recover $T$ but not sufficient to extract higher-frequency dynamics. Figure 16 shows that when the is-OMD algorithm is applied to uniformly sampled data, high-frequency dynamics are not recovered. When non-uniform sampling strategies are used, although $\unicode[STIX]{x0394}t_{av}=10\unicode[STIX]{x1D6FF}t$ , there is enough phase variation due to changes in $\unicode[STIX]{x0394}t$ to extract further frequencies. Both random strategies recover the dynamics within ranges determined by the Nyquist sampling criterion corresponding to $\unicode[STIX]{x0394}t_{min}$ , which are indicated in figure 16 with dotted lines. In both cases an additional pair of eigenvalues corresponding to frequencies beyond recoverable bounds is identified. Additionally, it can be observed that higher deviation from $\unicode[STIX]{x0394}t_{av}$ positively impacts the accuracy of the recovered dynamics, not only in terms of number of recovered frequencies but also in the estimation of $\unicode[STIX]{x1D6FD}$ .

Figure 16. Eigenvalue lattice for different sampling strategies.